Brian Douglas for OpenSauced

Posted on Feb 2 • Originally published at opensauced.pizza

AI Grant Traction in OSS Startups

#ai #opensource

Last year Nat Friedman and Daniel Gross kicked off the AI Grant and have incubated 60 companies with the pre-seed and seed funding.

Applications are open once again and I was curious to see what open source projects benefited from participating in the previous batches. My thesis is that open source provides an organic opportunity for growth and traction for AI startups.

This list was put together by tracking all public GitHub organizations that took AI Grant funding on OpenSauced. There I was able to quickly see project activity and which projects have an open source developer community alongside that matches up with the traction and engagement.

The OpenSauced AI Grant recipient list is public and viewable here.

Rowy

You know, Rowy is open-source, and it's not just for developers. I always tell this story when I start working with new engineering teams. I once built an entire database for my employer's marketing site using Google Docs and a bunch of code. Only later did I discover that companies were offering this as a service - they're called CMSs! Thankfully, I survived my over-engineering days, and now there's an even better solution: using a spreadsheet-like interface to manage a database without even realizing it.

What's cool about Rowy is that it opens up your database to the rest of your team since they probably already know how to use a spreadsheet. Low-code tools are getting more popular by the day, and Rowy stands out by using GPT-3 to generate projects and workflows.

Not too long ago, Rowy launched BuildShip, which takes things up a notch by helping users create workflows for no-code tools. You might think that no-code tools come with limitations and lock-ins, but BuildShip is here to change that. It's compatible with over a million npm packages and offers one-click deployment options to various cloud platforms like AWS and Azure.

Rowy has been around for a few years and it looks like the majority of the recent contributions still mostly come from their cofounders (marked in OpenSauced as maintainers), but it seems like they have decent traction with over 5k stars and almost 500 forks.

While searching through their GitHub repo, I saw they engage the community by allowing them to upvote items on the roadmap. This seems like a clever way to build a highly engaged userbase, and it shows.

rowyio / rowy

Low-code backend platform. Manage database on spreadsheet-like UI and build cloud functions workflows in JS/TS, all in your browser.

✨ Airtable-like UI for managing database ✨ Build any automation, with or without code ✨

Connect to your database and create Cloud Functions in low-code - without leaving your browser.
Focus on building your apps Low-code for Firebase and Google Cloud

Website • Documentation • Chat with us • Twitter

Live Demo 🛝

💥 Explore Rowy on live demo playground 💥

Features ✨

20211004-RowyWebsite.mp4

Powerful spreadsheet interface for Firestore

CMS for Firestore
CRUD operations
Bulk import or export data - csv, json, tsv
Sort and filter by row values
Lock, Freeze, Resize, Hide and Rename columns
Multiple views for the same collection

Automate with cloud functions and ready made extensions

Build cloud functions workflows on field level data changes
- Use any NPM modules or APIs
Connect to your favourite tool with pre-built code blocks or create your own
- SendGrid, Algolia, Twilio, Bigquery and more

Rich and flexible data fields

30+…

View on GitHub

Stats from the repo:

⭐ 5.5k
👀 58
Forks: 457
License: Apache-2.0

Replicate

Replicate allows you to run machine learning models using a cloud API without needing to be an expert in machine learning or infrastructure management. You can run open-source models published by others, or package and publish your own, either publicly or privately. The great part is that many of these models are open-source on GitHub, making it an excellent resource for learning.

Think of cogs as GitHub repositories that you host on Replicate for private or public use in your projects. Managing and hosting ML models can take several paths, most of which require a strong background in MLOps, which can be intimidating for beginners.

With Replicate, you can explore hosted cogs and easily integrate them into your project with just a few lines of code. I highly recommend browsing through some hosted models for inspiration for your AI side project.
Explore some hosted replicate cogs

A number of the example cogs are made from Zeke the founding Designer at Replicate. Thanks to cog model, the approach towards contribution to the Replicate open source project is really approachable and will be a driving force for more users adopting shared models on their platform.

The secret sauce for a lot of open source is making the project extensible and cogs are proving that model. Their community is growing fast and onboarding new hosted cogs every day, some with up to 100k runs.

replicate / cog

Containers for machine learning

Cog: Containers for machine learning

Cog is an open-source tool that lets you package machine learning models in a standard, production-ready container.

You can deploy your packaged model to your own infrastructure, or to Replicate.

Highlights

📦 Docker containers without the pain. Writing your own Dockerfile can be a bewildering process. With Cog, you define your environment with a simple configuration file and it generates a Docker image with all the best practices: Nvidia base images, efficient caching of dependencies, installing specific Python versions, sensible environment variable defaults, and so on.
🤬️ No more CUDA hell. Cog knows which CUDA/cuDNN/PyTorch/Tensorflow/Python combos are compatible and will set it all up correctly for you.
✅ Define the inputs and outputs for your model with standard Python. Then, Cog generates an OpenAPI schema and validates the inputs and outputs with Pydantic.
🎁 Automatic HTTP prediction server: Your model's types are used…

View on GitHub

Stats from the repo:

⭐ 6.4k
👀 62
Forks: 434
License: Apache-2.0

Chroma

Every time we've experienced a major shift in computing - from mainframes to PCs, the web, mobile devices, and the cloud - we've seen the birth of a new software stack. In this latest stack, AI takes charge of the application logic layer. It's programmed using natural language, and it's incredibly flexible. But there's more to the story than just logic - we can't forget about memory.

To handle the memory, storage, and state layers, we need a completely new approach: AI programmable memory. This goes beyond simply storing and fetching information; it actually determines what info is accessible to the AI. This is crucial for making AI systems reliable, controllable, easy to understand, and safe.

Enter Vector Databases: these specialized databases are designed to efficiently store, manage, and work with high-dimensional vector data. They're not like your traditional databases with tables or documents - instead, Vector Databases use mathematical representations of data points in multi-dimensional space. This makes searching and retrieving information faster and more accurate, especially when dealing with huge data sets or complex queries.

By taking advantage of advanced indexing and search algorithms, Vector Databases boost the performance and scalability of AI applications. They're an essential ingredient for developing next-gen AI systems, and that's where Chroma comes in. It's a Vector Database that lets users create LLMs (Language Learning Models) to power intuitive AI experiences for their users.

I recently sat down with one of the Chroma founders to discuss their unique position in the space and how they stand out from other solutions.

Their secret sauce seems to make vector searching and building knowledge bases accessible. Not only do they work in Python, a well-known language for AI/ML, but they also have JavaScript and Rust SDKs supported by the community.

chroma-core / chroma

the AI-native open-source embedding database

Chroma - the open-source embedding database.
The fastest way to build Python or JavaScript LLM apps with memory

| | Docs | Homepage

pip install chromadb # python client
# for javascript, npm install chromadb!
# for client-server mode, chroma run --path /chroma_db_path

The core API is only 4 functions (run our 💡 Google Colab or Replit template):

import chromadb
# setup Chroma in-memory, for easy prototyping. Can add persistence easily!
client = chromadb.Client()
# Create collection. get_collection, get_or_create_collection, delete_collection also available!
collection = client.create_collection("all-my-documents")

# Add docs to the collection. Can also update and delete. Row-based API coming soon!
collection.add(
    documents=["This is document1", "This is document2"], # we handle tokenization, embedding, and indexing automatically. You can skip that and add your own embeddings as well
    metadatas=[{"source": "notion"

…

View on GitHub

Stats from the repo:

⭐ 10.8k
👀 73
Forks: 882
License: Apache-2.0

Submit to the AI Grant

Gaining traction for startups can be even more challenging than building a project. Securing funding and exposure creates a win-win situation, and these projects have all benefited from being accessible to the open-source community. My thesis on open-source is validated as AI startups gain significant traction by making their projects open and extending them to the community.

If you are considering building or launching an AI project in 2024, I recommend considering submitting for the AI Grant before the deadline, February 16th. I wasn’t able to cover all open source projects that received a grant, but here a few more worth checking out:

And if you like to see the longer list of previous open source submissions checkout oss.fyi/aigrant.